Big Data Hadoop Developer Course with Handson

Course Curriculum

Chapter 1: Module 1:- Introduction to Big Data and Hadoop

Lecture 1: 1.1 Introduction to BigData

Lecture 2: 1.2 Types of Data

Lecture 3: 1.3 Introduction to Hadoop

Lecture 4: 1.4 Comparison with RDBMS

Lecture 5: 1.5 Hadoop Features

Lecture 6: 1.6 Hadoop Ecosystem

Lecture 7: 1.7 Hadoop Core Components

Chapter 2: Module 2- HDFS(Hadoop Distributed File System)

Lecture 1: 2.1 Hadoop Distributed File System

Lecture 2: 2.2 HDFS Files and Blocks

Lecture 3: 2.3 HDFS Components and Architecture

Lecture 4: 2.4 HDFS File Read-Write

Lecture 5: 2.5 HDFS Commands

Chapter 3: Module 3- Mapreduce

Lecture 1: 3.1 Mapreduce

Lecture 2: 3.2 Map-Reduce Operation

Lecture 3: 3.3 Map-reduce Example

Lecture 4: 3.4 HDFS Input Splits

Lecture 5: 3.5 MapReduce Architecture

Lecture 6: 3.6 Combiners and Partitioners

Lecture 7: 3.7 MapReduce Data Flow

Lecture 8: 3.8 MapReduce Examples

Chapter 4: Module 4- Advanced Mapreduce-I

Lecture 1: 4.1 Advanced MapReduce I

Lecture 2: 4.2 GenericOptionsParser, Tool and ToolRunner

Lecture 3: 4.3 Serialization and Deserialisation

Lecture 4: 4.4 Chaining of Jobs

Lecture 5: 4.5 Distributed Cache

Lecture 6: 4.6 Counters

Lecture 7: 4.7 JUnit Testing

Lecture 8: 4.8 Schedulers

Lecture 9: 4.9 Data Compression in Hadoop

Lecture 10: 4.10 Different Input and Output Formats in MapReduce

Lecture 11: 4.11 Chain Mapping

Lecture 12: 4.12 Compression in Gzip

Lecture 13: 4.13 Distributed cache

Lecture 14: 4.14 Counters

Lecture 15: 4.15 MRUnit Test

Lecture 16: 4.16 Multiple inputs

Lecture 17: 4.17 ReadSequence File

Chapter 5: Module 5- Apache Pig

Lecture 1: 5.1 Introduction to Apache Pig

Lecture 2: 5.2 PIG Latin language

Lecture 3: 5.3 Running PIG in Different Modes

Lecture 4: 5.4 Apache PIG Architecture

Lecture 5: 5.5 Grunt Shell

Lecture 6: 5.6 Pig Latin Statements

Lecture 7: 5.7 Pig Data Model- Scalar Types

Lecture 8: 5.8 Complex Types

Lecture 9: 5.9 Arithmetic Operators

Lecture 10: 5.10 Comparison Operators

Lecture 11: 5.11 Cast Operator

Lecture 12: 5.12 Type Construction Operators

Lecture 13: 5.13 Relational Operators

Lecture 14: 5.14 Loading and Storing

Lecture 15: 5.15 Filtering Operators

Lecture 16: 5.16 Grouping and Joining Operator- Part 1

Lecture 17: 5.17 Grouping and Joining Operator- Part 2

Lecture 18: 5.18 Combining and Splitting Operators

Lecture 19: 5.19 Sorting Operators

Lecture 20: 5.20 Diagnostic Operators

Lecture 21: 5.21 Filtering Operators-Pig Streaming with Python

Chapter 6: Module 6- Apache Hive

Lecture 1: 6.1 What is Hive

Lecture 2: 6.2 Hive Use Case@ Twitter

Lecture 3: 6.3 Hive vs MapReduce

Lecture 4: 6.4 What is Hive

Lecture 5: 6.5 Advantages of HiveQL

Lecture 6: 6.6 Hive Architecture

Lecture 7: 6.7 Data Types in Hive

Lecture 8: 6.8 Hive Query Language

Lecture 9: 6.9 DDL on DataBase

Lecture 10: 6.10 DDL on Tables

Lecture 11: 6.11 Different Tables in Hive

Lecture 12: 6.12 Advanced DDL on Tables

Lecture 13: 6.13 File Format in Hive

Lecture 14: 6.14 DML- Loading Data into tables

Lecture 15: 6.15 Managing Output

Lecture 16: 6.16 HiveQL-Queries

Lecture 17: 6.17 Operators and Functions in Hive

Lecture 18: 6.18 Hive Clauses

Chapter 7: Apache HBase: NoSQL Database for Hadoop

Lecture 1: 7.1 What is HBase

Lecture 2: 7.2 HBase history

Lecture 3: 7.3 Building blocks of hbase

Lecture 4: 7.4 Column family in Hbase

Lecture 5: 7.5 Storage of Column Family

Lecture 6: 7.6 Data Model in HBase

Lecture 7: 7.7 Timestamp as Versions

Lecture 8: 7.8 Getting Started with HBase Shell

Lecture 9: 7.9 DDL in HBase part-1

Lecture 10: 7.10 DDL in HBase part-2

Lecture 11: 7.11 DDL in HBase part-3

Lecture 12: 7.12 DML in HBase